Body Mass Index Trajectories Across the Adult Life Course and Pancreatic Cancer Risk

Overcoming underestimation of the association of excess weight with pancreatic cancer due to prediagnostic weight loss: Umbrella review of systematic reviews, meta-analyses, and pooled-analyses

Elevated body mass index (BMI) is linked to increased pancreatic cancer (PC) risk. Cancer-associated weight loss can occur years before the malignancy is diagnosed. This might have led to underestimation of the BMI-PC association. However, it is unknown if and to what extent this issue has been considered in previous epidemiological studies. We searched two databases through February 19, 2024 for systematic reviews, meta-analyses, and pooled analyses examining the BMI-PC association. We extracted information on study design with a special focus on the article's examination of prediagnostic weight loss as a potential source of bias, as well as how included cohort studies addressed this concern. Thirteen review articles, meta-analyses, and pooled analyses were identified. Only five (four pooled analyses, one systematic review) considered prediagnostic weight loss in their analyses. Twenty-four of 32 identified cohort studies reported having excluded initial years of follow-up. However, only 13 studies reported results after such exclusions, and effect estimates generally increased with longer periods of exclusion. We conclude that the association of overweight and obesity with PC risk is likely larger than suggested by published epidemiological evidence. Future studies should pay careful attention to avoid or minimize potential bias resulting from prediagnostic weight loss.

Integrating plasma protein-centric multi-omics to identify potential therapeutic targets for pancreatic cancer

BACKGROUND

Deciphering the role of plasma proteins in pancreatic cancer (PC) susceptibility can aid in identifying novel targets for diagnosis and treatment.

METHODS

We examined the relationship between genetically determined levels of plasma proteins and PC through a systemic proteome-wide Mendelian randomization (MR) analysis utilizing cis-pQTLs from multiple centers. Rigorous sensitivity analyses, colocalization, reverse MR, replications with varying instrumental variable selections and additional datasets, as well as subsequent meta-analysis, were utilized to confirm the robustness of significant findings. The causative effect of corresponding protein-coding genes' expression and their expression pattern in single-cell types were then investigated. Enrichment analysis, between-protein interaction and causation, knock-out mice models, and mediation analysis with established PC risk factors were applied to indicate the pathogenetic pathways. These candidate targets were ultimately prioritized upon druggability and potential side effects predicted by a phenome-wide MR.

RESULTS

Twenty-one PC-related circulating proteins were identified in the exploratory phase with no evidence for horizontal pleiotropy or reverse causation. Of these, 11 were confirmed in a meta-analysis integrating external validations. The causality at a transcription level was repeated for neutrophil elastase, hydroxyacylglutathione hydrolase, lipase member N, protein disulfide-isomerase A5, xyloside xylosyltransferase 1. The carbohydrate sulfotransferase 11 and histo-blood group ABO system transferase exhibited high-support genetic colocalization evidence and were found to affect PC carcinogenesis partially through modulating body mass index and type 2 diabetes, respectively. Approved drugs have been established for eight candidate targets, which could potentially be repurposed for PC therapies. The phenome-wide investigation revealed 12 proteins associated with 51 non-PC traits, and interference on protein disulfide-isomerase A5 and cystatin-D would increase the risk of other malignancies.

CONCLUSIONS

By employing comprehensive methodologies, this study demonstrated a genetic predisposition linking 21 circulating proteins to PC risk. Our findings shed new light on the PC etiology and highlighted potential targets as priorities for future efforts in early diagnosis and therapeutic strategies of PC.

Do Polygenic Risk Scores Add to Clinical Data in Predicting Pancreatic Cancer? A Scoping Review

BACKGROUND

Polygenic risk scores (PRS) summarize an individual's germline genetic risk, but it is unclear whether PRS offer independent information for pancreatic cancer risk prediction beyond routine clinical data.

METHODS

We searched 8 databases from database inception to March 10, 2023 to identify studies evaluating the independent performance of pancreatic cancer-specific PRS for pancreatic cancer beyond clinical risk factors.

RESULTS

Twenty-one studies examined associations between a pancreatic cancer-specific PRS and pancreatic cancer. Seven studies evaluated risk factors beyond age and sex. Three studies evaluated the change in discrimination associated with the addition of PRS to routine risk factors and reported improvements (AUCs: 0.715 to 0.745; AUC 0.791 to 0.830; AUC from 0.694 to 0.711). Limitations to clinical applicability included using source populations younger/healthier than those at risk for pancreatic cancer (n = 10), exclusively of European ancestry (n = 13), or controls without relevant exposures (n = 1).

CONCLUSIONS

While most studies of pancreatic cancer-specific PRS did not evaluate the independent discrimination of PRS for pancreatic cancer beyond routine risk factors, three that did showed improvements in discrimination.

IMPACT

For pancreatic cancer PRS to be clinically useful, they must demonstrate substantial improvements in discrimination beyond established risk factors, apply to diverse ancestral populations representative of those at risk for pancreatic cancer, and use appropriate controls.

Exploration of Risk Factors for Pancreatic Cancer and Development of a Clinical High-Risk Group Rating Scale

(1) Background: There are few studies on people at high risk for clinical pancreatic cancer (PC). We aimed to explore the risk factors of PC and establish a scale for identifying high-risk populations of clinical PC. (2) Methods: We conducted a matched case-control study, retrospectively collecting demographic data and common clinical indicators from all subjects. Logistic regression was used to explore the risk factors of PC. Based on these factors, we created a high-risk population rating scale, which showed a higher diagnostic value. (3) Results: 385 cases and 428 controls were finally enrolled in our study. Multivariate analysis showed that body mass index (BMI) below 18.5 kg/m2 (OR 5.944, 95%CI: 1.759~20.084), smoking (OR 2.745, 95%CI: 1.555~4.844), new-onset diabetes (OR 5.239, 95%CI: 2.091~13.125), low high-density lipoprotein cholesterol (HDL-C) levels (OR 1.790, 95%CI: 1.044~3.069), and carbohydrate antigen 19-9 (CA19-9) levels no less than 35 U/mL (OR 160.328, 95%CI: 83.392~308.243) were associated with an increased risk of PC, whereas high total cholesterol (TC) levels were related to a lower risk of PC (OR 0.392, 95%CI: 0.211~0.730). The high-risk population scale, whose area under the receiver operating curve reached 0.948 (p < 0.001), showed a greater clinical diagnostic value. (4) Conclusions: Smoking history, new-onset diabetes, BMI, TC, HDL-C, and CA19-9 levels were associated with the risk of PC. The high-risk population rating scale might be used for early clinical PC screening.